Qian Chen
I am currently pursuing the Ph.D. degree of the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. My current research is focusing on interpretable deep learning and intelligent mechanical fault diagnosis.
Basic information
Related links:
Education background:
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2020 - now, studying for Ph.D degree of mechanical Engineering at Shanghai Jiao Tong University, Shanghai, China. -
2016 - 2020, received Bachelor degree of Mechatronic Engineering at Harbin Institute of Technology, Harbin, China.
Interests:
- Mechanical fault diagnosis based on deep neural networks.
- Interpretable AI.
Publication
Paper:
- Q. Chen, et al., “Interpreting what typical fault signals look like via prototype-matching,” Advanced Engineering Informatics, vol. 62, p. 102849, Oct. 2024, doi: 10.1016/j.aei.2024.102849. (IF=8.0, TOP)
- Q. Chen, et al., “TFN: An interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis,” Mechanical Systems and Signal Processing, vol. 207, p. 110952, Jan. 2024, doi: 10.1016/j.ymssp.2023.110952. [Code | 中文介绍] (IF=7.9, TOP)
- X. Dong*, Q. Chen*, et al., “A systematic framework of constructing surrogate model for slider track peeling strength prediction,” Science China Technological Sciences, Sep. 2024, doi: 10.1007/s11431-024-2764-5. [Link | Introduction | 中文介绍] (IF=4.4)
- 陈钱, 等. 一种面向机械设备故障诊断的可解释卷积神经网络[J]. 机械工程学报, 2024, 60(12): 65. [中文介绍 | Link]
- 陈钱, 等. 基于迭代式局部加权线性回归的汽车座椅滑轨剥离强度预测[J]. 机械工程学报, 2024. (复审)
Patent:
- X. Dong, Q. Chen, et al., “Data-driven-based automobile seat slide rail peel strength prediction method”, CN116822292A, Sep. 29, 2023.
Project
- 2021.09-2023.03: Development of a predictive system for studying the robustness of car seat slide rail shapes. [detail | 详情 ]
- 2021.08-2022.08: Exploring the interpretability of convolutional neural networks combined with time-frequency transform. [detail | 详情]
- 2020.06-2021.06: Load identification methods for piping systems. [detail]